sbatch -J c_stats -o LOGS/cluster_stats.out -e LOGS/cluster_stats.err -c 8 --export=ALL --wrap="module load devel/Miniforge/Miniforge3 && module load bioinfo/FROGS/FROGS-v5.0.2 && cluster_stats.py --input-biom FROGS/denoising_swarm.biom --log-file FROGS/aff_filters.log --html FROGS/cluster_stats.html --hierarchical-clustering && module unload bioinfo/FROGS/FROGS-v5.0.2"

(to see all settings: cluster_stats.py --help)

Let look at the HTML file to see the result after cluster statistic on denoising result.
This report shows statistics on clusters and a hierarchical clustering of samples.


In the statistical cluster, you have two panels: “Cluster distribution” and “Sample distribution.”
Let's start with “Cluster distribution.” First, you have the number of clusters/ASVs that you obtain after using the denoising tool here, and the number of sequences in all of your samples.

• After denoising, we obtain 142,515 clusters for 1,156,905 sequences.

Clusters size summary
You have information about the distribution of clusters. After denoising, it is normal to obtain a majority of clusters that are formed from only a single sequence.

• Here, 116,335 clusters are formed from only one sequence.

• This represents 81,63% of clusters.

Sequences distribution The graph "Cumulative sequences proportion by cluster size" is interactive. You can select an area to zoom in on.
Within this framework, 81.06% of clusters are smaller than or equal to 699, corresponding to 22.73% of the total sequences.

• Approximately 82% of clusters are size 1, but this represents 10.06% of sequences.

This graph can therefore help you determine how much sequence you will lose when you use the abundance parameter in cluster filters.

Samples distribution

Sequences count You can see the distribution of clusters and sequences within each sample. You can sort the columns by clicking on .

Hierarchical clustering
You can only obtain graphs with cluster_stat, and there are other parameters you can use to select the distances between samples.